Information processing system, computer system, information processing method, and program

ABSTRACT

An information processing system includes a first system and a second system which are ready to communicate with each other. The first system transforms first data including specific information into second data not including the specific information and outputs the second data to the second system. The second system performs machine learning using the second data to generate a second learning deliverable and outputs the second learning deliverable to the first system. The first system obtains, based on at least a part of the first data and the second learning deliverable provided by the second system, the first learning deliverable.

TECHNICAL FIELD

The present disclosure generally relates to an information processingsystem, a computer system, an information processing method, and aprogram. More particularly, the present disclosure relates to aninformation processing system, a computer system, an informationprocessing method, and a program, all of which are configured ordesigned to perform machine-learning-related processing.

BACKGROUND ART

Patent Literature 1 teaches a method for providing data analysisservice. In Patent Literature 1, data collected by a company (dataowner) is transmitted, as training data, to a data analysis serviceprovider. The service provider generates a model (learned model) byanalyzing the data and sends the model back to the data owner. Thisallows the data owner to make, for example, a prediction by using themodel. In addition, Patent Literature 1 also teaches preserving businessconfidential information by anonymizing, when generating the trainingdata, variables of the data collected by the data owner.

In this manner, in Patent Literature 1, the machine learning isperformed by using the training data generated based on the collecteddata which does not include the business confidential information(specific information). However, this type of machine learning may causea decline in learning effectiveness because the specific information ismissing.

CITATION LIST Patent Literature

Patent Literature 1: US 2017/0061311 A

SUMMARY OF INVENTION

The problem is to provide an information processing system, a computersystem, an information processing method, and a program, all of whichcontribute to increasing learning effectiveness of machine learningwhile taking measures for preserving information securely.

An information processing system according to an aspect of the presentdisclosure is an information processing system for generating a firstlearning deliverable based on first data including specific information.The information processing system includes a first system and a secondsystem which are ready to communicate with each other. The first systemincludes a transformation unit which transforms the first data intosecond data not including the specific information and outputs thesecond data to the second system. The second system includes a learningunit which performs machine learning using the second data provided bythe first system to generate a second learning deliverable and outputsthe second learning deliverable to the first system. The first systemincludes a generation unit which obtains, based on at least a part ofthe first data and the second learning deliverable provided by thesecond system, the first learning deliverable.

A computer system according to another aspect of the present disclosureis a computer system functioning as the first system of the informationprocessing system described above.

A computer system according to still another aspect of the presentdisclosure is a computer system functioning as the second system of theinformation processing system described above.

An information processing method according to yet another aspect of thepresent disclosure is an information processing method for generating afirst learning deliverable based on first data including specificinformation. The information processing method according to this aspectincludes transforming the first data into second data that does notinclude the specific information, generating a second learningdeliverable by performing machine learning using the second data, andobtaining, based on at least a part of the first data and the secondlearning deliverable, the first learning deliverable.

A program according to still another aspect of the present disclosure isdesigned to cause one or more processors to perform the informationprocessing method described above.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of an information processing system accordingto an exemplary embodiment;

FIG. 2 illustrates a production system;

FIG. 3 illustrates transformation processing to be performed by theinformation processing system; and

FIG. 4 is a sequence chart for the information processing system.

DESCRIPTION OF EMBODIMENTS 1. Embodiments 1.1 Overview

FIG. 1 shows an information processing system 100 according to anexemplary embodiment. As shown in FIG. 4, the information processingsystem 100 is an information processing system for generating a firstlearning deliverable R1 based on first data D1 including specificinformation. The information processing system 100 includes a firstsystem 10 and a second system 20 which are ready to communicate witheach other. As shown in FIG. 1, the first system 10 includes atransformation unit 131 which transforms the first data D1 into seconddata D2, not including the specific information, and which outputs thesecond data D2 to the second system 20. The second system 20 includes alearning unit 221 which performs machine learning using the second dataD2 provided by the first system 10 to generate a second learningdeliverable R2 and output the second learning deliverable R2 to thefirst system 10. The first system 10 includes a generation unit 132which obtains, based on at least a part of the first data D1 and thesecond learning deliverable R2 provided by the second system 20, thefirst learning deliverable R1.

This information processing system 100 uses the second data D2 withoutthe specific information, instead of the first data D1 with the specificinformation, when the machine learning is performed by the second system20 which is ready to communicate with the first system 10. That is tosay, the machine learning may be performed by the second system 20 withthe specific information held in the first system 10. Therefore, thesecond system 20 may use, with no need to care about preservation ofinformation, a computational resource having higher computing power thanthe first system 10 and installed at a different facility from afacility where the first system 10 is installed. Meanwhile, the firstsystem 10 obtains the first learning deliverable R1 based on the secondlearning deliverable R2 provided by the second system 20 and at least apart of the first data D1. Therefore, the specific information notincluded in the second data D2 that has been used for the machinelearning by the second system 20 may be reflected in the first learningdeliverable R1. Thus, this information processing system 100 allowsincreasing the learning effectiveness of machine learning while takingmeasures for preserving information securely.

1.2. Details

Next, the information processing system 100 will be described in furtherdetail with reference to FIGS. 1-4.

The information processing system 100 is configured to generate thefirst learning deliverable R1 based on the first data D1 (see FIG. 4).

The first data D1 is data representing a mount board 300 (see FIGS. 2and 3) where a plurality of electronic components 320 are mounted on aboard (circuit board) 310. The first data D1 may contain various typesof information about the shapes and sizes of the board 310 and theplurality of electronic components 320, the types of the board 310 andthe plurality of electronic components 320, the arrangement of theplurality of electronic components 320 (specifically, their arrangementon the board 310), and the arrangement of an electrical path (circuitpattern) of the board 310. A copper-clad laminate may be cited as anexample of the board 310. Examples of the electronic components 320include resistors, capacitors, coils, diodes, transformers, IC chips,connectors, and switches. The first data D1 may be, for example, CADdata for the mount board 300.

The first learning deliverable R1 may include at least one of a learnedmodel or output based on the learned model. In this embodiment, thefirst learning deliverable R1 is a learned model for obtaining aparticular result in response to the first data D1 that has been input.The particular result is control data for a production system 200 (seeFIG. 2), and data, according to which production tact time of the mountboard 300 will satisfy criteria. As shown in FIG. 2, the productionsystem 200 includes a plurality (e.g., four in the example illustratedin FIG. 2) of mounters 211-214. The mounters 211-214 are used to arrangethe electronic components 320 at predetermined locations on the board310. In this embodiment, the control data includes information aboutassignment (assignment information) of the electronic components 320 tothe mounters 211-214. That is to say, the assignment informationindicates which of the mounters 211-214 is used to assign each of theplurality of electronic components 320 with respect to the mount board300. For example, the control data represents a result of grouping ofthe plurality of electronic components 320. In other words, theplurality of electronic components 320 are classified into first tofourth groups corresponding to the mounters 211-214, respectively. Theresult of grouping of the plurality of electronic components 320 mayaffect the time it takes for the production system 200 to produce themount board 300 (tact time). The tact time is determined depending on acombination of the electronic components and the mounter. For example,the tact time is determined by the total traveling distance that anozzle head of the mounter need to go to mount all electroniccomponents. Appropriately grouping the plurality of electroniccomponents 320 allows the time for producing the mount board 300 to beshortened, which would contribute to improving the productionefficiency. From this point of view, the first learning deliverable R1is preferably control data indicating the result of the best grouping ofthe plurality of electronic components 320. Therefore, the learned modelthat is the first learning deliverable R1 of the information processingsystem 100 is obtained by learning the relationship between the firstdata D1 and the control data so as to provide, in response to the firstdata D1 that has been input, the best control data for the production ofthe mount board 300 by the production system 200.

As shown in FIG. 1, the information processing system 100 includes thefirst system 10 and the second system 20. The first system 10 and thesecond system 20 are ready to communicate with each other. Morespecifically, the first system 10 and the second system 20 are ready tocommunicate with each other via a communications network 30. Thecommunications network 30 may include the Internet. The communicationsnetwork 30 does not have to be a network compliant with a singlecommunications protocol but may also be made up of a plurality ofnetworks compliant with mutually different communications protocols. Thecommunications protocol may be selected from various types of well-knownwired and wireless communications standard. The communications network30 is shown in a simplified form in FIG. 1 but may include datacommunications devices such as a repeater hub, a switching hub, abridge, a gateway, and a router.

As shown in FIG. 1, the first system 10 includes an input/output unit11, a communications unit 12, and a processing unit 13.

The input/output unit 11 is an interface for inputting/outputting data.The input/output unit 11 enables input of the first data D1, and outputof the first learning deliverable R1. The communications unit 12 servesas a communications interface. The communications unit 12 may beconnected to the communications network 30 and has the function ofestablishing communication over the communications network 30. Thecommunications unit 12 is compliant with a predetermined communicationsprotocol. The predetermined communications protocol may be selected fromvarious well-known wired and wireless communication standards. Theprocessing unit 13 is configured to perform overall control on the firstsystem 10, i.e., configured to control the input/output unit 11 and thecommunications unit 12. The processing unit 13 may be implemented as,for example, a computer system including one or more processors(microprocessors) and one or more memories. That is to say, the one ormore processors perform the function of the processing unit 13 byexecuting a program (application) stored in the one or more memories. Inthis embodiment, the program is stored in advance in the one or morememories of the processing unit 13. However, this is only an example andshould not be construed as limiting. The program may also be downloadedvia a telecommunications line such as the Internet or distributed afterhaving been stored in a non-transitory storage medium such as a memorycard. The first system 10 like this may be implemented as a computersystem.

As shown in FIG. 1, the second system 20 includes a communications unit21 and a processing unit 22. The communications unit 21, as well as thecommunications unit 12, may be connected to the communications network30, and has the function of establishing communication over thecommunications network 30. The communications unit 21 is compliant witha predetermined communications protocol. The predeterminedcommunications protocol may be selected from various well-known wiredand wireless communication standards. The processing unit 22 isconfigured to perform overall control on the second system 20, i.e.,control the communications unit 21. The processing unit 22 may beimplemented as, for example, a computer system including one or moreprocessors (microprocessors) and one or more memories. That is to say,the one or more processors perform the function of the processing unit22 by executing a program (application) stored in the one or morememories. In this embodiment, the program is stored in advance in theone or more memories of the processing unit 22. However, this is only anexample and should not be construed as limiting. The program may also bedownloaded via a telecommunications line such as the Internet ordistributed after having been stored in a non-transitory storage mediumsuch as a memory card. The second system 20 like this may be implementedas a computer system.

As shown in FIG. 1, the processing unit 13 of the first system 10includes a transformation unit 131 and a generation unit 132. In FIG. 1,neither the transformation unit 131 nor the generation unit 132 has asubstantive configuration but both the transformation unit 131 and thegeneration unit 132 represent functions to be performed by theprocessing unit 13. Moreover, as shown in FIG. 1, the processing unit 22of the second system 20 includes a learning unit 221. The learning unit221 does not have a substantive configuration but represents a functionto be performed by the processing unit 22.

The transformation unit 131 is configured to transform the first data D1into second data D2 and output the second data D2 to the second system20. More specifically, the transformation unit 131 generates the seconddata D2 on receiving the first data D1 via the input/output unit 11 andtransmits the second data D2 to the second system 20 via thecommunications unit 12. In this embodiment, the second data D2 is datawithout the specific information which is included in the first data D1.The first data D1 is data representing the mount board 300 (see FIGS. 2and 3). As described above, the first data D1 may contain various typesof information about the shapes and sizes of the board 310 and theplurality of electronic components 320, the types of the board 310 andthe plurality of electronic components 320, the arrangement of theplurality of electronic components 320 on the board 310, and thearrangement of the electrical path (circuit pattern) of the board 310.The specific information is information which may be used only in thefirst system 10 but may not be used in the second system 20. In thisembodiment, in the first data D1, each of various pieces of informationabout the types of the plurality of electronic components 320, thearrangement of the plurality of electronic components 320, and thearrangement of the electrical path (circuit pattern) on the board 310 ishandled as the specific information. That is to say, the second data D2may be the same data as the first data D1 except information about thetypes of the plurality of electronic components 320, the arrangement ofthe plurality of electronic components 320, and the arrangement of theelectrical path on the board 310.

The transformation unit 131 generates the second data D2 by replacingthe specific information of the first data D1 with alternativeinformation which may be an alternative to the specific information.That is to say, the second data D2 includes the alternative informationas an alternative to the specific information. As used herein, thealternative information may be information irrelevant to the specificinformation or may also be information obtained by performing apredetermined type of processing on the specific information. Forexample, if the specific information is information about the types ofthe plurality of electronic components 320, the alternative informationmay be information about alternative types of the plurality ofelectronic components 320. In that case, the transformation unit 131performs, as a predetermined type of processing, the processing ofchanging the types of the plurality of electronic components 320 inaccordance with a certain rule or randomly. Alternatively, if thespecific information is information about the arrangement of theplurality of electronic components 320, the alternative information maybe information about an alternative arrangement of the plurality ofelectronic components 320. In that case, the transformation unit 131performs, as a predetermined type of processing, the processing ofchanging the arrangement of the plurality of electronic components 320in accordance with a certain rule or randomly. Still alternatively, ifthe specific information is information about the arrangement of theelectrical path on the board 310, the alternative information may beinformation showing, as information irrelevant to the specificinformation (i.e., information which is irrelevant to the contents ofthe first data D1), a state in which the board 310 has no electricalpath.

FIG. 3 illustrates the processing of transforming the first data D1 intothe second data D2 to be performed by the transformation unit 131. InFIG. 3, the mount board 300 corresponds to the specifics represented bythe first data D1, while the mount board 300A corresponds to thespecifics represented by the second data D2. As can be seen from FIG. 3,the mount board 300A represented by the second data D2 is different fromthe mount board 300 represented by the first data D1. Therefore, nospecific information (such as information about the types of theplurality of electronic components 320, the arrangement of the pluralityof electronic components 320, and the arrangement of the electrical pathon the board 310) can be extracted from the second data D2.

The learning unit 221 is configured to perform machine learning usingthe second data D2 provided by the first system 10 to generate thesecond learning deliverable R2 and output the second learningdeliverable R2 to the first system 10. More specifically, the learningunit 221 generates the second learning deliverable R2 on receiving thesecond data D2 from the first system 10 and transmits the secondlearning deliverable R2 to the first system 10 via the communicationsunit 21. The second learning deliverable R2 may include at least one ofa learned model or output based on the learned model. In thisembodiment, the second learning deliverable R2 may include a learnedmodel for obtaining a particular result in response to the second dataD2 that has been input. The particular result is control data for theproduction system 200 (see FIG. 2), and data, according to whichproduction tact time of the mount board 300 will satisfy criteria. Asthe machine learning, reinforcement learning (deep reinforcementlearning, in particular) may be used. The learning unit 221 generates,by machine learning, the learned model that provides the control data inresponse to the second data D2 that has been input. Then, the learningunit 221 outputs, as the second learning deliverable R2, a learned modelthat provides control data that will result in the shortest tact time,to the first system 10. Note that as the method of the machine learningitself, well-known methods may be adopted, and therefore, description ofthe specific learning method will be omitted herein.

The generation unit 132 is configured to obtain, based on at least apart of the first data D1 and the second learning deliverable R2provided by the second system 20, the first learning deliverable R1.More specifically, the generation unit 132 obtains, on receiving thesecond learning deliverable R2 from the second system 20 via thecommunications unit 12, the first learning deliverable R1 based on atleast a part of the first data D1 and the second learning deliverableR2, and outputs the first learning deliverable R1 via the input/outputunit 11. In this case, whether to use the first data D1 entirely or onlypartially depends on what type of first learning deliverable R1 is to beobtained. In this embodiment, the second learning deliverable R2 is alearned model obtained by the machine learning using the second data D2.The generation unit 132 obtains the first learning deliverable R1 byperforming the machine learning using the first data D1 and the learnedmodel included in the second learning deliverable R2. As the machinelearning, reinforcement learning (deep reinforcement learning, inparticular) is used. That is to say, the generation unit 132 uses, as amodel for machine learning, the learned model generated by the secondsystem 20, instead of a model on which no learning has been performedyet (unlearned model). Therefore, the first system 10 does not have tohave processing capacity for performing the machine learning from thebeginning. As the machine learning, reinforcement learning (deepreinforcement learning, in particular) is used as in the learning unit221 of the second system 20. The generation unit 132 applies the firstdata D1 to the learned model, according to which the tact time withrespect to the second data D2 will satisfy the criteria. In this manner,the generation unit 132 obtains, by machine learning, the learned model,according to which the tact time with respect to the first data D1satisfies the criteria, as the first learning deliverable R1. Note thatthe first learning deliverable R1 does not have to be the learned modelbut may also be the best solution for the processing relevant to thefirst data D1. For example, the first learning deliverable R1 may be thecontrol data for the first data D1 and data according to which the tacttime satisfies the criteria (data that will provide the shortest tacttime). The generation unit 132 may obtain a learned model by performingthe machine learning using the first data D1 and the learned modelincluded in the second learning deliverable R2. Then, the generationunit 132 may output, as the first learning deliverable R1, the controldata obtained by introducing the first data D1 to this learned model.

1.3. Operation

Next, operation of the information processing system 100 will be brieflydescribed with reference to the sequence chart shown in FIG. 4. Thefollowing example shows how the information processing system 100 mayoperate in a situation where the user 40 inputs the first data D1 to thefirst system 10 to obtain the first learning deliverable R1 from thefirst system 10.

When the user 40 inputs the first data D1 to the first system 10, thefirst system 10 transforms the first data D1 into the second data D2 andoutputs the second data D2 to the second system 20. In this example, thesecond data D2 does not include the specific information unlike thefirst data D1. On receiving the second data D2 from the first system 10,the second system 20 performs the machine learning using the second dataD2, thereby obtaining the second learning deliverable R2. The secondsystem 20 outputs the second learning deliverable R2 to the first system10. On receiving the second learning deliverable R2 from the secondsystem 20, the first system 10 obtains the first learning deliverable R1by using the first data D1 and the second learning deliverable R2. Then,the first system 10 provides the first learning deliverable R1 to theuser 40.

The user 40 who has been provided with the first learning deliverable R1by the information processing system 100 may obtain a particular resultfrom the first data D1 by using the first learning deliverable R1. Theparticular result is the control data for a production system 200 (seeFIG. 2), and the data that allows production tact time of the mountboard 300 to satisfy the criteria. As a result, the productionefficiency of the mount board 300 by the production system 200 may beimproved.

1.4. Recapitulation

As can be seen from the foregoing description, an information processingsystem 100 is an information processing system for generating a firstlearning deliverable R1 based on first data D1 including specificinformation. The information processing system 100 includes a firstsystem 10 and a second system 20 which are ready to communicate witheach other. As shown in FIG. 1, the first system 10 includes atransformation unit 131 which transforms the first data D1 into seconddata D2, not including the specific information, and which outputs thesecond data D2 to the second system 20. The second system 20 includes alearning unit 221 which performs machine learning using the second dataD2 provided by the first system 10 to generate a second learningdeliverable R2 and output the second learning deliverable R2 to thefirst system 10. The first system 10 includes a generation unit 132which obtains, based on at least a part of the first data D1 and thesecond learning deliverable R2 provided by the second system 20, thefirst learning deliverable R1. In this manner, the informationprocessing system 100 allows increasing the learning effectiveness ofmachine learning while taking measures for preserving the informationsecurely.

In other words, it can be said that the information processing system100 performs the following method (information processing method). Theinformation processing method is an information processing method forgenerating a first learning deliverable R1 based on first data D1 thatincludes specific information. The information processing methodincludes transforming the first data D1 into second data D2 that doesnot include the specific information, generating a second learningdeliverable R2 by performing machine learning using the second data D2,and generating, based on at least a part of the first data D1 and thesecond learning deliverable R2, the first learning deliverable R1. Inthis manner, the information processing method, as well as theinformation processing system 100, allows increasing the learningeffectiveness of machine learning while taking measures for preservingthe information securely.

The information processing system 100 is implemented as a computersystem (including one or more processors). That is to say, theinformation processing system 100 may have its function performed byhaving the one or more processors execute a program (computer program).This program is a program designed to cause the one or more processorsto perform the information processing method. This program, as well asthe information processing method, allows increasing the learningeffectiveness of machine learning while taking measures for preservinginformation securely.

2. Variations

Note that the embodiment described above is only an exemplary one ofvarious embodiments of the present disclosure and should not beconstrued as limiting. Rather, the exemplary embodiment may be readilymodified in various manners depending on a design choice or any otherfactor without departing from the scope of the present disclosure. Next,variations of the exemplary embodiment will be enumerated one afteranother.

In one variation, the second learning deliverable R2 may include aplurality of learning deliverables which are obtained by machinelearning using the second data D2. For example, the learning unit 221generates, by machine learning, a learned model which provides controldata in response to the second data D2 that has been input. The learningunit 221 obtains, as the learning deliverables, a plurality of learnedmodels that provide control data, according to which the tact time willsatisfy a predetermined criteria. The learning unit 221 outputs, as thesecond learning deliverable R2, the plurality of learned models(learning deliverables) to the first system 10. In this case, thegeneration unit 132 may adopt, as the first learning deliverable R1, oneof the plurality of learned models included in the second learningdeliverable R2. For example, the generation unit 132 selects, based onthe evaluation results of the plurality of learning deliverablesobtained by using the first data, a model to be suitably used as thefirst learning deliverable R1 from the plurality of learned models. Thegeneration unit 132 obtains control data by introducing the first dataD1 to the plurality of learning deliverables, and adopts, as the firstlearning deliverable R1, a learning deliverable which provides controldata that will result in the shortest tact time. In this case, there isno need for the generation unit 132 to perform the machine learningunlike the embodiment described above, thus allowing reducing aprocessing load on the first system 10.

Note that the plurality of learning deliverables may be control dataobtained from a learned model, instead of the learned model itself. Forexample, the learning unit 221 generates, by machine learning, a learnedmodel which provides control data in response to the second data D2 thathas been input. The learning unit 221 generates, based on the learnedmodel, multiple items of control data according to which the tact timewill satisfy predetermined criteria, as the learning deliverable. Thelearning unit 221 outputs, as the second learning deliverable R2, themultiple items of control data (learning deliverable) to the firstsystem 10. In this case, the generation unit 132 adopts, as the firstlearning deliverable R1, one of the multiple items of control dataincluded in the second learning deliverable R2. For example, thegeneration unit 132 selects, based on the evaluation results of theplurality of learning deliverables obtained by using the first data, anitem of control data to be suitably used as the first learningdeliverable R1 from the multiple items of control data. The generationunit 132 evaluates the tact time based on the first data D1 and thecontrol data, and adopts, as the first learning deliverable R1, alearning deliverable (control data) that will result in the shortesttact time. In this case, the first learning deliverable R1 is not thelearned model but the best solution for the processing relevant to thefirst data D1.

In another variation, the transformation unit 131 may generate thesecond data D2 by simply removing the specific information from thefirst data D1. Nevertheless, replacing the specific information withalternative information is a better choice in order to use the firstdata D1 and the second data D2 in the same form.

In still another variation, the control data may include, as analternative or in addition to the information about assignment of theelectronic components 320 to the mounters 211-214, at least one type ofinformation out of information about the order in which the electroniccomponents 320 are arranged or information about the order in which theelectronic components 320 are mounted on the board 310.

In yet another variation, the production system 200 may include anadditional piece of equipment other than the mounter or may include nomounters. That is to say, the production system 200 may include at leastone type of equipment selected from the group consisting of mounters,insertion machines (e.g., a high-speed axial component insertionmachine, a high-speed jumper wire insertion machine, and a high-densityradial component insertion machine), chip mounters, screen printers, andlaser markers.

In yet another variation, the information processing system 100 is alsoapplicable to generating data for various types of systems other thanthe production system 200. For example, the information processingsystem 100 may provide, as the first learning deliverable R1, a learnedmodel for an authentication system. In this variation, the firstlearning deliverable R1 may be a learned model which provides a resultof authentication or identification in response to the first data D1that has been input. For example, there are various types ofidentification such as authentication of persons, identification ofpersons, authentication of creatures (e.g., animals such as cats anddogs), and identification of objects (e.g., cars). The following exampleis how to identify a person using this system 100. In this case, thefirst data D1 is an image of a person. The specific information may beinformation about the privacy of the person. Examples of the specificinformation include information about parts of the person's face (e.g.,eyes, nose, mouth). In the first system 10, the transformation unit 131generates, based on the given first data D1, second data D2 that doesnot include the specific information. For example, the transformationunit 131 may generate the second data D2 by partially replacing theimage of the person's face with another image as alternativeinformation. Then, the first system 10 outputs the second data D2 to thesecond system 20. In this variation, the first system 10 transformsmultiple items of first data D1 into multiple items of second data D2and outputs the multiple items of second data D2 to the second system20. In response, in the second system 20, the learning unit 221 performsmachine learning on the second data D2, thereby generating a learnedmodel. Then, the second system 20 outputs, as the second learningdeliverable R2, the learned model to the first system 10. As the machinelearning to be performed at this stage, deep learning with or without ateacher may be appropriately used. The first system 10 obtains the firstlearning deliverable R1 based on the first data D1 and the secondlearning deliverable R2. The first system 10 performs machine learningusing the first data D1 on the learned model that is the second learningdeliverable R2, thereby generating a learned model. Then, the firstsystem 10 provides the learned model as the first learning deliverableR1. As the machine learning to be performed at this stage, deep learningwith or without a teacher may be used appropriately as in the secondsystem 20. In this manner, the information processing system 100 maymake the first system 10 generate a learned model for identifying aperson even without passing the specific information included in thefirst data D1 from the first system 10 to the second system 20. Notethat the generation unit 132 may select, if the second system 20provides the second learning deliverable R2 including, as the learningdeliverable, a plurality of learned models for the first system 10, alearned model to be used as the first learning deliverable R1 from theplurality of learned models. For example, the generation unit 132identifies, based on the plurality of learned models provided, theperson by using the first data D1 and adopts a learned model with thebest evaluation as the first learning deliverable R1.

More specifically, the first data D1 includes a plurality of face imagesfor use in person identification and identification informationindicating whose face each of the plurality of face images represents.The number of the face images used is not particularly limited, but manyface images are preferably used. The first system 10 generates seconddata D2 including an image, obtained by blacking out the eye part ofeach face image included in the first data D1, and the identificationinformation of the face image and outputs the second data to the secondsystem 20. In this case, the specific information is information aboutthe eye part of the face image. In the second system 20, the learningunit 221 performs the machine learning on the second data D2, therebygenerating a learned model for identifying a person. Then, the secondsystem 20 outputs, as the second learning deliverable R2, the learnedmodel to the first system 10. In response, the first system 10 performs,by using the given second learning deliverable R2 as an initial valuefor a learned model, machine learning using the first data D1 aslearning data. Then, the first system 10 outputs, as the first learningdeliverable R1, a learned model updated by the machine learning.Alternatively, the first system 10 may output, when the user requestsperson identification with respect to a particular image, anidentification result, obtained by the updated learned model, as thefirst learning deliverable R1. Note that the first system 10 may use,when converting the first data D1 to the second data D2, an image ofwhich the eye part is replaced with a common eye image as an alternativeto the face image, of which the eye part is blacked out. The secondlearning deliverable R2 does not have to be the learned model but mayalso be, when the user requests person identification with respect to aparticular image, an identification result presenting top N candidatesfor the person to be identified which are obtained from the learnedmodel. Specifically, when N=3, the second learning deliverable R2 may bethe identification result presenting Persons X, Y, and Z. The generationunit 132 searches the top N candidates for the identificationinformation of a person, whose eye feature quantity (i.e., featurequantity obtained from the specific information) is closest to that ofthe image for which the person identification is requested, and outputsthe person's identification information as the first learningdeliverable R1. In this case, the eye feature quantity may be types ofinformation about, for example, the ratio of eye width to face width andwhether the eyes are located near or far from the nose. In the exampledescribed above, the specific information is information about the eyepart of the face image. However, the specific information is not limitedto this, but may also be any particular part of the face image. Examplesof the particular parts include eyes, nose, mouth, ears, eyebrows, andcombinations thereof.

In yet another variation, the information processing system 100 mayinclude a plurality of first systems 10. In that case, the specificinformation is held in each of the first systems 10 and is nottransmitted to the second system 20. Therefore, even if the plurality offirst systems 10 are used by multiple different users, information maybe preserved appropriately. In addition, as the second system 20, acomputer system having high computing power may be used for the purposeof machine learning. Alternatively, the second system 20 may performlearning, even if individual first systems 10 are operated by differentorganizations or operators and the size of the teacher data held by theindividual first systems 10 is small, by collecting multiple items offirst data D1 of the plurality of first systems 10. This allowsimproving the learning performance.

In yet another variation, the information processing system 100 may beimplemented as a system for determining the name of a patient's diseasebased on the patient's diagnostic image (e.g., X-ray images, CT images,MRI images, and pathological images) and medical record informationincluding his or her identification information (e.g., patient's age andrace). As for a disease from which only a small number of patients aresuffering, it is difficult to collect a plenty of medical recordinformation about the disease. Moreover, if the information about thepatient's age and race included in the medical record information andthe name of the patient's disease are disclosed, then the patient couldbe easily identified by the medical record, since only a small number ofpatients are suffering from the disease.

More specifically, the information processing system 100 may beimplemented as a system for estimating the name of the patient's diseasebased on the medical record information. In that case, the first data D1is learned data including the medical record information and theidentification information indicating the name of a patient's disease bythe medical record information. The first system 10 generates the seconddata D2 by removing, from the first data D1, the specific informationsuch as information about the patient's age and race that belongs to hisor her privacy and that could possibly be misused to identify him orher. Then, the first system 10 outputs the second data D2 to the secondsystem 20. In the second system 20, the learning unit 221 generates,using the second data D2, a learned model for determining the diseasename. Then, the second system 20 outputs, as the second learningdeliverable R2, the learned model to the first system 10. The firstsystem 10 acquires, based on the learned model provided as the secondlearning deliverable R2, top N candidates for the disease name withrespect to the medical record information designated by the user. Thefirst system 10 rearranges the above N candidates in the descendingorder in which patients, belonging to the age group written on themedical record information designated by the user, are likely to catchthe disease. Then, the first system 10 outputs, as the first learningdeliverable R1, the top N candidates that are rearranged. As analternative or in addition to this, the first system 10 may rearrangethe top N candidates in the descending order in which patients of therace, written on the medical record information designated by the user,are likely to catch the disease. Then, the first system 10 may output,as the first learning deliverable R1, the top N candidates that arerearranged. Note that the order of the names of the diseases thatpatients of respective age groups or races are likely to catch may bestored in a database in advance.

The first system 10 may be a system to have a network, in whichidentification information including the patient's age and race is addedto an input layer of a learned model as the second learning deliverableR2, learn by using the first data D1. In general, a convolution neuralnetwork (CNN) is used for deep learning by which an image is used asinput data for identification purposes. The CNN is a network made up ofa first part which creates a feature map based on an input imageobtained from a convolutional layer and a pooling layer, and a secondpart which obtains, through a fully connected layer, final output fromthe feature map. In this variation, the second system 20 creates, as thesecond learning deliverable R2, a learned model by the CNN with an X-rayimage used as an input image. When performing the machine learning usingthe first data D1, the first system 10 establishes, based on the secondlearning deliverable R2, a network having a different structure from thesecond learning deliverable R2. The generation unit 132 establishes, asthe first learning deliverable R1, a network to which the identificationinformation indicating the patient's age and race, as well as the X-rayimage, has been entered as input data. The network established by thegeneration unit 132, as well as the second learning deliverable R2, isalso a CNN and also made up of the first part and the second part. Thefirst part of the first learning deliverable R1 is the same as the firstpart of the second learning deliverable R2. On the other hand, thesecond part of the first learning deliverable R1 uses, in addition tothe feature map obtained by the first part, the identificationinformation indicating the patient's age and race as the input data. Thegeneration unit 132 generates the first learning deliverable R1 byperforming the machine learning using the first data D1.

In yet another variation, the information processing system 100 (thefirst system 10 and the second system 20) may include a plurality ofcomputers. For example, the functions of the information processingsystem 100 (in particular, the transformation unit 131, the generationunit 132, and the learning unit 221) may be distributed in multipledifferent devices. Alternatively, at least some functions of the secondsystem 20 may be implemented as, for example, a cloud (cloud computingsystem) as well. Nevertheless, from a point of view of the preservationof information, the cloud (cloud computing system) is preferably notused as the first system 10.

The agent that performs the functions of the information processingsystem 100 (including the first system 10 and the second system 20)described above includes a computer system. The computer system mayinclude, as hardware components, a processor and a memory. The functionsto be performed by the information processing system 100 according tothe present disclosure that plays the role of such an agent are carriedout by making the processor execute a program stored in the memory ofthe computer system. The program may be stored in advance in the memoryof the computer system. Alternatively, the program may also bedownloaded through a telecommunications line or be distributed afterhaving been recorded in some non-transitory storage medium such as amemory card, an optical disc, or a hard disk drive, any of which isreadable for the computer system. The processor of the computer systemmay be implemented as a single or a plurality of electronic circuitsincluding a semiconductor integrated circuit (IC) or a large-scaleintegrated circuit (LSI). Optionally, a field-programmable gate array(FPGA) to be programmed after an LSI has been fabricated, an applicationspecific integrated circuit (ASIC), or a reconfigurable logic deviceallowing the connections or circuit sections inside of an LSI to bereconfigured may also be adopted as the processor. Those electroniccircuits may be either integrated together on a single chip ordistributed on multiple chips, whichever is appropriate. Those multiplechips may be integrated together in a single device or distributed inmultiple devices without limitation.

3. Aspects

As can be seen from the foregoing description of embodiments and theirvariations, the present disclosure has the following aspects. In thefollowing description, reference signs are inserted in parentheses justfor the sake of clarifying correspondence in constituent elementsbetween the following aspects of the present disclosure and theexemplary embodiments described above.

A first aspect is an information processing system (100) for generatinga first learning deliverable (R1) based on first data (D1) includingspecific information. The information processing system (100) includes afirst system (10) and a second system (20) which are ready tocommunicate with each other. The first system (10) includes atransformation unit (131) which transforms the first data (D1) intosecond data (D2) not including the specific information and outputs thesecond data (D2) to the second system (20). The second system (20)includes a learning unit (221) which performs machine learning using thesecond data (D2) provided by the first system (10) to generate a secondlearning deliverable (R2) and outputs the second learning deliverable(R2) to the first system (10). The first system (10) includes ageneration unit (132) which obtains, based on at least a part of thefirst data (D1) and the second learning deliverable (R2) provided by thesecond system (20), the first learning deliverable (R1). This aspectallows increasing the learning effectiveness of machine learning whiletaking measures for preserving information securely.

A second aspect is a specific implementation of the informationprocessing system (100) according to the first aspect. In the secondaspect, the second learning deliverable(R2) includes a learned modelobtained by the machine learning using the second data (D2). This allowsincreasing the learning effectiveness of machine learning while takingmeasures for preserving information securely.

A third aspect is a specific implementation of the informationprocessing system (100) according to the second aspect. In the thirdaspect, the generation unit (132) performs the machine learning usingthe first data (D1) and the learned model included in the secondlearning deliverable (R2). This aspect allows generating, based on thelearned model generated by the second system (20), a learned model moresuitable for the first data (D1).

A fourth aspect is a specific implementation of the informationprocessing system (100) according to the first aspect. In the fourthaspect, the second learning deliverable (R2) includes a plurality oflearning deliverables obtained by the machine learning using the seconddata (D2). This aspect allows increasing the learning effectiveness ofmachine learning while taking measures for preserving the informationsecurely.

A fifth aspect is a specific implementation of the informationprocessing system (100) according to the fourth aspect. In the fifthaspect, the first learning deliverable (R1) is selected, based onevaluation results of the plurality of learning deliverables obtained byusing the first data (D1), from the plurality of learning deliverables.This aspect allows reducing a processing load on the first system (10).

A sixth aspect is a specific implementation of the informationprocessing system (100) according to any one of the first to fifthaspects. In the sixth aspect, the second data (D2) includes alternativeinformation to be an alternative to the specific information. Thisaspect facilitates application of the first data (D1) to the secondlearning deliverable (R2).

A seventh aspect is a specific implementation of the informationprocessing system (100) according to the sixth aspect. In the seventhaspect, the alternative information is information having no relativityto the specific information. This aspect allows preserving theinformation more securely.

An eighth aspect is a specific implementation of the informationprocessing system (100) according to the sixth aspect. In the eighthaspect, the alternative information is information obtained byperforming predetermined processing on the specific information. Thisaspect allows preserving the information even more securely.

A ninth aspect is a specific implementation of the informationprocessing system (100) according to any one of the first to eighthaspects. In the ninth aspect, the first learning deliverable (R1) iseither a learned model for obtaining a particular result in response tothe first data (D1) that has been input or an optimal solution forprocessing relevant to the first data (D1). This aspect allowsincreasing the learning effectiveness of machine learning while takingmeasures for preserving information securely.

A tenth aspect is a specific implementation of the informationprocessing system (100) according to any one of the first to ninthaspects. In the tenth aspect, the first data (D1) is data representing amount board (300) where a plurality of electronic components (320) aremounted on a board (310). The specific information includes at least onetype of information selected from the group consisting of: informationabout types of the plurality of electronic components (320); informationabout an arrangement of the plurality of electronic components (320);and information about an arrangement of an electrical path on the board(310). This aspect allows obtaining data for optimizing the productionefficiency of the mount board (300).

An eleventh aspect is a specific implementation of the informationprocessing system (100) according to any one of the first to tenthaspects. In the eleventh aspect, the information processing system (100)includes a plurality of first systems (10), one of which is the firstsystem (10). This aspect allows increasing the learning effectiveness ofmachine learning while taking measures for preserving informationsecurely.

A twelfth aspect is a computer system which functions as the firstsystem (10) of the information processing system (100) of any one offirst to eleventh aspects. This aspect allows increasing the learningeffectiveness of machine learning while taking measures for preservinginformation securely.

A thirteenth aspect is a computer system which functions as the secondsystem (20) of the information processing system (100) of any one offirst to eleventh aspects. This aspect allows increasing the learningeffectiveness of machine learning while taking measures for preservinginformation securely.

A fourteenth aspect is an information processing method for generating afirst learning deliverable (R1) based on first data (D1) includingspecific information. The information processing method includestransforming the first data (D1) into second data (D2) that does notinclude the specific information. The information processing method alsoincludes generating a second learning deliverable (R2) by performingmachine learning using the second data (D2). The information processingmethod further includes obtaining, based on the first data (D1) and thesecond learning deliverable (R2), the first learning deliverable (R1).This aspect allows increasing the learning effectiveness of machinelearning while taking measures for preserving information securely. Inparticular, in the fourteenth aspect, the information processing methodincludes making the first system (10) transform the first data (D1) intosecond data (D2) that does not include the specific information. Theinformation processing method also includes generating the secondlearning deliverable (R2) by making a second system (20), different fromthe first system (10), perform machine learning using the second data(D2). In addition, the information processing method may further includemaking the first system (10) obtain, based on at least a part of thefirst data (D1) and the second learning deliverable (R2), the firstlearning deliverable (R1). This aspect allows increasing the learningeffectiveness of machine learning while taking measures for preservinginformation securely.

A fifteenth aspect is a program designed to cause one or more processorsto perform the processing method according to the fourteenth aspect.This aspect allows increasing the learning effectiveness of machinelearning while taking measures for preserving information securely.

REFERENCE SIGNS LIST

1 Information Processing System

10 First System

131 Transformation Unit

132 Generation Unit

20 Second System

221 Learning Unit

300 Mount Board

310 Board

320 Electronic Component

D1 First Data

R1 First Learning Deliverable

D2 Second Data

R2 Second Learning Deliverable

1. An information processing system configured to generate a firstlearning deliverable based on first data, the first data includingspecific information, the information processing system comprising: afirst system and a second system which are configured to be ready tocommunicate with each other, the first system including a transformationunit configured to transform the first data into second data notincluding the specific information and to output the second data to thesecond system, the second system including a learning unit configured toperform machine learning using the second data provided by the firstsystem to generate a second learning deliverable and output the secondlearning deliverable to the first system, the first system including ageneration unit configured to obtain, based on at least a part of thefirst data and the second learning deliverable provided by the secondsystem, the first learning deliverable.
 2. The information processingsystem of claim 1, wherein the second learning deliverable includes alearned model obtained by the machine learning using the second data. 3.The information processing system of claim 2, wherein the generationunit is configured to perform the machine learning using the first dataand the learned model included in the second learning deliverable. 4.The information processing system of claim 1, wherein the secondlearning deliverable includes a plurality of learning deliverablesobtained by the machine learning using the second data.
 5. Theinformation processing system of claim 4, wherein the first learningdeliverable is selected, based on evaluation results of the plurality oflearning deliverables obtained by using the first data, from theplurality of learning deliverables.
 6. The information processing systemof claim 1, wherein the second data includes alternative information tobe an alternative to the specific information.
 7. The informationprocessing system of claim 6, wherein the alternative information isinformation having no relativity to the specific information.
 8. Theinformation processing system of claim 6, wherein the alternativeinformation is information obtained by performing predeterminedprocessing on the specific information.
 9. The information processingsystem of claim 1, wherein the first learning deliverable is either alearned model for obtaining a particular result in response to the firstdata that has been input or an optimal solution for processing relevantto the first data.
 10. The information processing system of claim 1,wherein the first data is data representing a mount board where aplurality of electronic components are mounted on a board, and thespecific information includes at least one type of information selectedfrom the group consisting of: information about types of the pluralityof electronic components; information about an arrangement of theplurality of electronic components; and information about an arrangementof an electrical path on the board.
 11. The information processingsystem of claim 1, comprising a plurality of first systems, one of whichis the first system.
 12. A computer system configured to function as thefirst system of the information processing system of claim
 1. 13. Acomputer system configured to function as the second system of theinformation processing system of claim
 1. 14. An information processingmethod for generating a first learning deliverable based on first data,the first data including specific information, the informationprocessing method comprising: transforming the first data into seconddata that does not include the specific information; generating a secondlearning deliverable by performing machine learning using the seconddata; and obtaining, based on at least a part of the first data and thesecond learning deliverable, the first learning deliverable.
 15. Anon-transitory storage medium storing a program designed to cause one ormore processors to perform the information processing method of claim14.